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Financial Statement Analysis with Large Language Models

papers.ssrn.com

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Re: Financial Statement Analysis with Large Language Models

#21

Post by yaj54 »

foota wrote: post #12
infecto wrote: post #11

Earlier quoted context omitted.

Was going to point out the same. Glad to have the paper to read but I don't think the findings are significant.

I agree this isn't earth shattering, but I think the benefit here is that it's a general solution instead of one trained on financial statements specifically.

agreed. most people can't create a custom tailored finance statement model. but many people can write the following sentence: "analyze this financial statement and suggest a market strategy." and if that sentence performs as well as an (albeit old) custom model, and is likely to have compound improvements in its performance over time with no changes to the instruction sentence...

Re: Financial Statement Analysis with Large Language Models

#23

Post by Kon-Peki »

apwell23 wrote: post #20
primitivesuave wrote: post #13

The area where I see this making the most transformational change is by enabling average citizens to ask meaningful questions about the finances of their local government. In Cook County, Illinois, there are hundreds of local municipalities and elected authorities, all of which are producing monthly financial statements. There is not enough citizen oversight and rarely any media attention except in the most egregious…

> citizens to ask meaningful questions about the finances of their local government. is there a demand for this. I live in cook country. I really don't want to ask these questions. Not sure what I get out of asking these questions other than anger and frustration.

Municipal bond rating agencies should be a client of such data.

And if not the rating agencies, people who invest in municipal bonds.

Re: Financial Statement Analysis with Large Language Models

#24

Post by bethekind »

flourpower471 wrote: post #15
antimatter15 wrote: post #9

Figure 3 on p.40 of the paper seems to show that their LLM based model does not statistically significantly outperform a 3 layer neural network using 59 variables from 1989. This figure compares the prediction performance of GPT and quantitative models based on machine learning. Stepwise Logistic follows Ou and Penman (1989)’s structure with their 59 financial predictors. ANN is a three-layer artificial neural networ…

Not to mention, as somebody who works in quant trading doing ml all day on this kind of data. That ann benchmark is nowhere near state of the art. People didn't stop working on this in 1989 - they realised they can make lots of money doing it and do it privately.

Do you use llama 3 for your work?

Re: Financial Statement Analysis with Large Language Models

#25

Post by yaj54 »

infecto wrote: post #18
foota wrote: post #14

Earlier quoted context omitted.

You could dump all of a companies financial statements together, or dump all of a companies competitors in with it, for one.

That was outside the context of what the paper was studying.

maybe the authors should have used a larger context window in order to output their paper ;-)

I jest - a well contained and specified context is important for a paper.

But also, questions like "it would be interesting to also try this other thing" is how new papers get written.

Re: Financial Statement Analysis with Large Language Models

#26

Post by m463 »

apwell23 wrote: post #20
primitivesuave wrote: post #13

The area where I see this making the most transformational change is by enabling average citizens to ask meaningful questions about the finances of their local government. In Cook County, Illinois, there are hundreds of local municipalities and elected authorities, all of which are producing monthly financial statements. There is not enough citizen oversight and rarely any media attention except in the most egregious…

> citizens to ask meaningful questions about the finances of their local government. is there a demand for this. I live in cook country. I really don't want to ask these questions. Not sure what I get out of asking these questions other than anger and frustration.

if all the citizens can ask these questions, I think it will make a difference.

and of course, the follow-up questions. Like who.

Re: Financial Statement Analysis with Large Language Models

#27

Post by infecto »

nicce wrote: post #22

What will happen if everyone starts using heavy statistical methods or LLMs to predict stocks prices? And buys stock based on them? Will it absolutely make everything unpredictable? Edit: assuming that they initially provide good predictions

This has already been a thing since the late 80s.

Re: Financial Statement Analysis with Large Language Models

#28

Post by surfingdino »

nicce wrote: post #22

What will happen if everyone starts using heavy statistical methods or LLMs to predict stocks prices? And buys stock based on them? Will it absolutely make everything unpredictable? Edit: assuming that they initially provide good predictions

HFT guys won't touch GPT. The stakes are too high. If LLMs could give those guys an edge they'd be all over this tech.

Re: Financial Statement Analysis with Large Language Models

#30

Post by abdullahkhalids »

primitivesuave wrote: post #13

The area where I see this making the most transformational change is by enabling average citizens to ask meaningful questions about the finances of their local government. In Cook County, Illinois, there are hundreds of local municipalities and elected authorities, all of which are producing monthly financial statements. There is not enough citizen oversight and rarely any media attention except in the most egregious…

The citizens ask LLMs (or more advanced future AIs) to identify if government finances are being used efficiently, and if there is evidence of corruption.

The corrupt government officers then start using the AIs to try to cover up the evidence of their crimes in the financial statements. The AI possibly putting the skills of high-end and expensive human accountants (or better) into the hands of local governments.

Who wins this attrition war?

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